Conversion of paper documents to electronic form speeds and enhances many business processes. Business documents often contain identifying information that allows documents to be routed or stored properly and there is great value in extracting this information automatically from scanned document images.
Business processes in the office are facilitated by networks of computers and so-called multifunction devices. These devices incorporate printers, faxes, and scanners that, coupled with servers running the proper software, create functionality to convert paper documents to electronic and vice-versa. Furthermore, these networked devices can connect personal digital assistants, cell phones, and other hand-held devices. It is therefore important and valuable to extract business information in a networked environment to store and display it where is it most useful.
Exchanging business cards is a tradition business practice for people to convey important information. The printed information on a business card may include a combination of identification and/or contact information; such as name, address, phone number, and/or email address; and non-important business information; such as logos, graphics, annotations, and/or slogans. Notwithstanding the fact that the cards contain both types of information, a business card is still considered a critical vehicle to conveying the information needed in establishing and/or maintaining business relationships.
With the advent of digital devices such as personal digital assistants, electronic address books, or personal computers, the converting of the printed information on the business card to an electronic format has become a necessary business tool. The conventional tools, scanned business card recognition system, often used to import the printed information to digital devices are labor-intensive and prone to error.
In conventional scanned business card recognition systems, once the printed text on a business card has been recognized by optical character recognition software, the text gets labeled according to one or more functions, e.g., email, name, organization, address, etc. Business card fields are often labeled using lexicographical information wherein text comprising names is identified using a dictionary and wherein text comprising organizations is identified using a dictionary of organizations, etc.
Another conventional method for converting of the printed information on the business card to an electronic format acquires the layout knowledge of business cards. The conventional method generates the layout knowledge of business cards from a predefined logical structure. However, in most cases, the conventional method cannot readily determine the physical relationships (or layout) among the items in a card because of the great variety in the physical layout of today's business cards.
Although conventional scanned business card recognition systems provide a variety of methodologies to convert the printed information on the business card to an electronic format, such systems are labor-intensive and prone to error. Such systems are limited by the dictionaries or pre-defined information repositories used to identify the text information or are limited in identifying the text because of the great variety in the physical layout of today's business cards.
Lastly, the various conventional methodologies of business card recognition convert the information into proprietary formats that are not easily transferable to other contact management systems. These conventional systems are also tethered to a particular electronic device such that business card cannot be readily process on business card recognition dependent devices and then transferred in a format that is universally acceptable to the user's desired device.
Business letters form another class of critical documents. Owing to the expense of paper handling, many businesses seek to eliminate paper workflows by scanning mail as soon as it is delivered to the mailroom. As with business cards, these are semi-structured documents where identifying information such as names, address, email addresses, telephone numbers, and the like appear in variable places within the document.
In a mailroom operations, many businesses, such as banks, attempt to “truncate the paper” by scanning documents as they enter the business and route them electronically. This is faster and cheaper than using hardcopy. To route efficiently, it is required that field of business letters be recognized and labeled. In particular, one usually requires the recipient and sender. The recipient's email address is looked up in a database and the scanned document is sent. Current optical character recognition (OCR) systems do not provide this high level structure from a scanned document.
Therefore, it is desirable to provide a business card recognition system that is automated and substantially error free. Moreover, it is desirable to provide a business card recognition system that converts the information into a format that is easily transferable to conventional contact management systems. Furthermore, it is desirable to provide a business card recognition system that is not tethered to a particular electronic device such that business card can be readily process on business card recognition independent devices and then transferred in a format that is universally acceptable to the user's desired device.
Therefore, it is desirable to provide a business letter recognition system that is automated and substantially error free. Moreover, it is desirable to provide a business letter recognition system that converts the information on a business letter into a format that is easily used to route the scanned version of the business letter to its rightful destination. Furthermore, it is desirable to provide a business letter recognition system that is not tethered to a particular electronic device such that business letter can be readily process on business letter recognition independent devices and then the electronic version of the business letter can be readily transferred or routed to its proper destination based upon the information from business letter recognition system.
Therefore, it is further desirable to provide a system and method that uses a multifunction device to scan a business letter to a repository; processes the business letter with an optical character recognition system; uses the provided layout information to segment the recognized lines of text into a linear sequence of regions and separators; identifies tokens for each line using classifiers; and for each separator, parses the token sequence into “non-terminal” symbols comprising region labels and uses the recipient field to route the scanned document.
A method distills information from a hard copy business card to generate a structured electronic file having the distilled information therein. The method electronically scans a platen area of a network citizen, having a business card thereon, to create a bitmap of the scanned platen area; transfers the bitmap of the scanned platen area to a network processor; segments the bitmap of the scanned platen area, using the network processor, into a bitmap object, the bitmap object corresponding to the scanned business card; converts, using the network processor, the bitmap object into a block of text; processes, using the network processor, the block of text to generate a structured representation of semantic entities corresponding to the scanned business card; and converts, using the network processor, the structured representation into a structure text file.
A system distills information from a hard copy business card to generate a structured electronic file having the distilled information therein. The system includes a network citizen having a platen area, said network citizen electronically scanning the platen area, having a business card thereon, to create a bitmap of the scanned platen area and a network processor, operatively connected to the network citizen, the network processor segmenting the bitmap of the scanned platen area, received from the network citizen, into a bitmap object, the bitmap object corresponding to the scanned business card. The network processor converts the bitmap object into a block of text and processing the block of text to generate a structured representation of semantic entities corresponding to the scanned business card. The network processor converts the structured representation into a structure text file.
A system retrieves information from a hard copy business card to generate a structured electronic file having the retrieved information therein. The system includes a network scanning means, having a platen area, for electronically scanning the platen area, having a business card thereon, to create a bitmap of the scanned platen area and a network server, operatively connected to the network scanning means. The network server includes segmentation means for segmenting the bitmap of the scanned platen area, received from the network scanning means, into a bitmap object, the bitmap object corresponding to the scanned business card; bitmap to text converter means, operatively connected to the segmentation means, for converting the bitmap object into a block of text; semantic means, operatively connected to the bitmap to text converter means, for processing the block of text to generate a structured representation of semantic entities corresponding to the scanned business card; and card generation means, operatively connected to the bitmap to text converter means, for converting the structured representation into a structure text file.
A method parses business card information text from electronically derived text representing an electronically scanned business card. The method orders lines of text linearly left-to-right, top-to-bottom; generates, for each line of text having a keyword therein, a terminal symbol corresponding to the keyword therein, the terminal symbol being a member of a pre-defined set of terminal symbols; generates, for each line of text not having a keyword therein and absent of numeric characters, an alphabetic terminal symbol; generates, for each line of text not having a keyword therein and having a numeric character therein, an alphanumeric terminal symbol; generates a string of terminal symbols from the generated terminal symbols; determines a probable parsing of the generated string of terminal symbols; labels each text line, according to a determined function, with non-terminal symbols; and parses the business card information text into fields of business card information text based upon the non-terminal symbol of each text line and the determined probable parsing of the generated string of terminal symbols.
A method distills information from a hard copy business document to determine routing information for forwarding of an electronic version of business document to a destination. The method electronically scans a platen area of a digital scanner, having a business document thereon, to create a bitmap of the scanned platen area; converts the bitmap object into a block of data, the block of data including lines of text, position information, text size information, and line separators; segments the block of data into text lines, separators, and region objects; assigns a lexical type to the segmented text lines based upon a predetermined set of regular expressions; identifies, within the block of data, recipient information, using the assigned lexical type information and a stochastic context-free grammar model; determines routing information corresponding to a destination associated with the identified recipient information; and forwards the electronic version of the scanned business document to the destination.
A method distills information from a hard copy business document to determine routing information for forwarding of an electronic version of business document to a destination. The method electronically scans a platen area of a digital scanner, having a business document thereon, to create a bitmap of the scanned platen area; converts the bitmap object into a block of data, the block of data including lines of text, position information, text size information, and line separators; segments the block of data into text lines, separators, and region objects; assigns a lexical type to the segmented text lines based upon a predetermined set of regular expressions; identifies, within the block of data, recipient information, using the assigned lexical type information and a stochastic context-free grammar model; orders, within the block of sender information, lines of text linearly left-to-right, top-to-bottom; generates, for each line of text having a keyword therein, a terminal symbol corresponding to the keyword therein, the terminal symbol being a member of a pre-defined set of terminal symbols; generates, for each line of text not having a keyword therein and absent of numeric characters, an alphabetic terminal symbol; generates, for each line of text not having a keyword therein and having a numeric character therein, an alphanumeric terminal symbol; generates a string of terminal symbols from the generated terminal symbols; determines a probable parsing of the generated string of terminal symbols; labels each text line, according to a determined function, with non-terminal symbols; parses the text into fields of business card information text based upon the non-terminal symbol of each text line and the determined probable parsing of the generated string of terminal symbols; processes the parsed text to generate a structured representation of semantic entities; converts the structured representation into a structure text file; determines routing information corresponding to a destination associated with the identified recipient information; forwards the structure text file to the destination.
A method distills information from an electronic business document to determine routing information for forwarding of the electronic version of business document to a destination. The method parses a rich text format document into lines of text, position information, text size information, and line separators; segments the parsed rich text format document into text lines, separators, and region objects; assigns a lexical type to the segmented text lines based upon a predetermined set of regular expressions; identifies, within the block of data, recipient information, using the assigned lexical type information and a stochastic context-free grammar model; determines routing information corresponding to a destination associated with the identified recipient information; and forwards the electronic version of the scanned business document to the destination.
A system distills information from a hard copy business document to determine routing information for forwarding of an electronic version of business document to a destination associated with an addressee of the hard copy business document. The system includes a digital network scanner to electronically scan a platen area, having a business document thereon, to create a bitmap of the scanned platen area and a network server operatively connected to the digital network scanner. The network server converts the bitmap object into a block of data, the block of data including lines of text, position information, text size information, and line separators and segments the block of data into text lines, separators, and region objects. The network server assigns a lexical type to the segmented text lines based upon a predetermined set of regular expressions and identifies, within the block of data, addressee information, using the assigned lexical type information and a stochastic context-free grammar model. The network server determines routing information corresponding to a destination associated with the identified addressee information and forwards the electronic version of the scanned business document to the destination associated with an addressee of the hard copy business document.
A system distills information from an electronic business document to determine routing information for forwarding of the electronic version of business document to a destination associated with an addressor and addressee of the electronic business document. The system includes a network connected document processing device to generate an electronic business document and a network server operatively connected to the network connected document processing device. The network server parses a rich text format document into lines of text, position information, text size information, and line separators and segments the parsed rich text format document into text lines, separators, and region objects. The network server assigns a lexical type to the segmented text lines based upon a predetermined set of regular expressions and identifies, within the block of data, addressor and addressee information, using the assigned lexical type information and a stochastic context-free grammar model. The network server determining routing information corresponding to a destination associated with the identified addressor and addressee information and forwards the electronic version of the scanned business document to the destination.
Networked multifunction devices and computers scan business letters and their attachments, process the scanned images with optical character recognition to extract lines of text, their positions, and font sizes, apply regular expression matching to assign tokens to the lines of text, apply stochastic-context-free grammar technology to extract names, addresses, email addresses and the like, and route the documents electronically to the intended recipient via email. Optical character recognition can occur on the scanner itself or on a connected computer. Similarly, the extraction functionality can reside on the scanning device or on a connected computer.